Credit card purchases of goods and services over the Internet are now a common practice. In a typical transaction, a consumer selects some number of items to purchase, provides information identifying themselves, such as name, address, and the like, and provides payment information typically in the form of a credit card.
There is, however, great risk associated with merchants doing business on the Internet. The anonymity of online customers makes the incidence of fraud incomparably higher for online merchants than for brick-and-mortar, in-house shopping venues, where the customer is present for all transactions. In March of 1999, Visa International, Inc. reported in Computerworld that less than 2% of their credit-card transactions occurred over the Internet, but that online transactions accounted for up to 50% of their disputed charges. Recent reports state that “one in five online buyers has reportedly been a victim of Net-related fraud . . . according to a National Consumers League survey.” The National Consumers League reported that between 1997 and 1998 there was a 600 percent increase in reports of Internet fraud.
Credit card transactions present various significant risks to the merchant. These include:
Loss of Payments & Merchandise
Online fraud presents the risk of huge revenue losses to Internet merchants. When fraud occurs, both the payment and the merchandise are gone. When consumers use credit cards in person and fraudulent transactions occur, issuing banks reimburse cardholders. Thus, if a cardholder's card is lost or stolen and then used by another fraudulently, it is the issuer that must absorb fraudulent charges, as federal law limits the cardholder's liability. This is because when a card is presented in person, the merchant does not have liability for fraudulent charges.
With Internet commerce, however, the merchants, not the banks, must reimburse issuers for 100% of the fraudulent purchases. Regulations created by Visa, MasterCard and other card issuer associations specify that in card-not-present (CNP) situations, merchants assume the full risk of purchases made by credit card, regardless of authorizations made by issuing banks. Because CNP transactions are the standard for e-commerce, and because these transactions will continue to grow in volume, the current framework shifts the burden of these losses primarily to the merchants. Indeed, for large companies, which do all of their business over the Internet based on CNP transactions, this loss exposure can be in the tens of millions of dollars.
Beyond using a stolen credit card or fabricated card number, other types of fraud abound in the online purchasing arena. With Internet orders, it's easier than ever for customers to dispute charges. For example, a husband may make charges and deny them to his wife, who in turn calls the card issuer and fervently disputes their bill. Though these purchases are valid, the card company may decide in favor of their customer, and the merchant is responsible for the chargebacks. In addition, merchants are responsible for loss from disputed charges when customers claim non-receipt of goods, non-arrival of goods due to incorrect shipping addresses, or goods refused on delivery.
Loss from Shipping Charges
The charges for shipping physical goods are normally paid by consumers. When online fraud occurs, however, shipping charges become the responsibility of the online merchant—yet another hit in the merchant's profitability.
Loss from Human Intervention
Online merchants attempting to use parameter-based rules and negative files to determine credit card fraud often watch their administrative costs skyrocket along with the increased staffing needed for time-consuming fraud investigations.
Loss of Valued Customers
Poorly managed fraud-control systems can lead to decreased customer satisfaction resulting in lost revenues. Many online retailers burden their customers with lengthy and complex online-order forms in an attempt to minimize fraud, consequently resulting in unacceptably elevated order abandonment, as legitimate customers become frustrated, lose interest in pursuing their purchases, and flee to other vendors. In the same vein, worthy customers become insulted when inaccurate rules turn down their orders. Such customers often permanently migrate to other websites where they find better treatment. The misguided merchants then waste valuable marketing dollars attempting to replace the very customers they turned away.
Loss from Chargeback Fees
A chargeback is a charge levied onto a merchant for the amount of a purchase that has previously been charged onto a payment card, and has been successfully disputed by a customer. When a chargeback is processed, the merchant must pay a fee of $15.00 to 25.00. If the merchant disputes the chargeback and wants to re-present, or re-issue, the charge, an equivalent fee is billed to the merchant. If the card issuer again decides in favor of the customer and processes a second chargeback, the merchant is charged yet another fee, and this time the charge cannot be re-presented. The merchant can, however, arbitrate the charge, whereupon the issuing bank can charge an up-front, non-refundable fee of up to $50.00.
Loss from Chargeback Fines
In addition to paying a fee for each chargeback, issuing banks can levy fines on merchants having too many chargebacks. Typically 1.5-3.0% of the merchant's chargeback volume, such fines can range from a few hundred dollars per month, to $10,000 or even $100,000 per month, with fines escalating higher as chargebacks continue unabated.
Loss of Credit Card Privileges
The final blow—after months of escalating fines, if chargebacks exceed a small percentage of a merchant's overall credit-card sales, the merchant can lose the privilege of receiving payment through credit card issuers.
Limitations of Most Fraud-Risk Solutions
On-line retailers have been quickly implementing a wide range of fraud fighting techniques to combat this perplexing and ubiquitous problem. The technological level of Internet fraud solutions today is similar to where credit card issuers were in 1992, when the incidence of bankcard fraud exploded and was expected to reach US $1 billion by 1995.
Typical solutions that have been deployed include the use or rule sets and basic checks such as address verification.
Rule Sets
Rule sets use if/then logic that attempts to identify aberrant behavior using limited data, including negative file information. Rule sets are difficult to implement because there is no real predictive capability associated with them. Throughout the 1990's, banks issuing credit cards quickly determined that rule-based fraud approaches by themselves were ineffective because they:                Allow an unacceptable level of fraud to flow through        Have high false-positive rates, where numerous valid orders are turned away compared to the small amount of fraudulent orders detected        Create a high volume of cases requiring human intervention        Are difficult to manage        Are slow to reflect new fraud situations        
Address Verification Service (AVS)
AVS was designed for mail and telephone orders (MOTOs), and many online merchants use AVS today because it is the only tool they have. AVS has no international fraud detection capability, is inconsistent in its results, and, while providing some value, it is not designed for online transactions, and is by no means considered a complete solution for Internet fraud detection and revenue optimization.
Accordingly, there remains the need to identify and prevent fraudulent credit card transactions occurring over public networks, such as the Internet.
In order to identify fraudulent consumer credit card transactions in an online environment (e.g., the Internet), it is desirable to create a profile for each buyer that summarizes the transactional behavior the buyer, preferably for as many of the buyer's Internet purchases as possible. Such a profile makes it possible to contrast a currently attempted transaction with the historical behavior pattern of the buyer to help ascertain the likelihood of fraud. Also, such profiles make it possible to identify a pattern of transactions that are individually benign, but in aggregate indicates a likelihood of fraud. Two examples make this point clearer.